
"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
      from ...onnx.config import PatchingSpec
      from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       str				=      '''longformer'''






       def __init__(	self			:				Tuple							,   UpperCAmelCase_			:				Union[List[int], int] = 512							,   UpperCAmelCase_			:				int = 2							,   UpperCAmelCase_			:				int = 1							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				int = 2							,   UpperCAmelCase_			:				int = 30_522							,   UpperCAmelCase_			:				int = 768							,   UpperCAmelCase_			:				int = 12							,   UpperCAmelCase_			:				int = 12							,   UpperCAmelCase_			:				int = 3_072							,   UpperCAmelCase_			:				str = "gelu"							,   UpperCAmelCase_			:				float = 0.1							,   UpperCAmelCase_			:				float = 0.1							,   UpperCAmelCase_			:				int = 512							,   UpperCAmelCase_			:				int = 2							,   UpperCAmelCase_			:				float = 0.02							,   UpperCAmelCase_			:				float = 1e-12							,   UpperCAmelCase_			:				bool = False							,   **UpperCAmelCase_			:				str							,   )->	List[Any]:







              """simple docstring"""
              super().__init__(pad_token_id=UpperCAmelCase_							,   **UpperCAmelCase_      )

              UpperCamelCase       			=		attention_window
              UpperCamelCase       			=		sep_token_id
              UpperCamelCase       			=		bos_token_id
              UpperCamelCase       			=		eos_token_id
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		layer_norm_eps
              UpperCamelCase       			=		onnx_export





class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				int							,   UpperCAmelCase_			:				"PretrainedConfig"							,   UpperCAmelCase_			:				str = "default"							,   UpperCAmelCase_			:				"List[PatchingSpec]" = None      )->	Union[str, Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		True






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                      ("global_attention_mask", dynamic_axis),
                  ]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              UpperCamelCase       			=		super().outputs
              if self.task == "default":
                     UpperCamelCase       			=		{0: "batch"}
              return outputs






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	float:







              """simple docstring"""
              return 1e-4






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	int:







              """simple docstring"""
              # needs to be >= 14 to support tril operator
              return max(super().default_onnx_opset							,   14      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase"							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(
                  preprocessor=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   seq_length=UpperCAmelCase_							,   is_pair=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              import torch

              # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
              # makes the export fail randomly
              UpperCamelCase       			=		torch.zeros_like(inputs["input_ids"]      )
              # make every second token global
              UpperCamelCase       			=		1

              return inputs







"""simple docstring"""


from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)


# General docstring
SCREAMING_SNAKE_CASE			  =  """MobileNetV1Config"""

# Base docstring
SCREAMING_SNAKE_CASE			  =  """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE			  =  [1, 1_024, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE			  =  """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE			  =  """tabby, tabby cat"""


SCREAMING_SNAKE_CASE			  =  [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_=None					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		{}

       if isinstance(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		model.mobilenet_va
       else:
              UpperCamelCase       			=		model

       UpperCamelCase       			=		"MobilenetV1/Conv2d_0/"
       UpperCamelCase       			=		backbone.conv_stem.convolution.weight
       UpperCamelCase       			=		backbone.conv_stem.normalization.bias
       UpperCamelCase       			=		backbone.conv_stem.normalization.weight
       UpperCamelCase       			=		backbone.conv_stem.normalization.running_mean
       UpperCamelCase       			=		backbone.conv_stem.normalization.running_var

       for i in range(13					):
              UpperCamelCase       			=		i + 1
              UpperCamelCase       			=		i * 2

              UpperCamelCase       			=		backbone.layer[pt_index]
              UpperCamelCase       			=		F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
              UpperCamelCase       			=		pointer.convolution.weight
              UpperCamelCase       			=		pointer.normalization.bias
              UpperCamelCase       			=		pointer.normalization.weight
              UpperCamelCase       			=		pointer.normalization.running_mean
              UpperCamelCase       			=		pointer.normalization.running_var

              UpperCamelCase       			=		backbone.layer[pt_index + 1]
              UpperCamelCase       			=		F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
              UpperCamelCase       			=		pointer.convolution.weight
              UpperCamelCase       			=		pointer.normalization.bias
              UpperCamelCase       			=		pointer.normalization.weight
              UpperCamelCase       			=		pointer.normalization.running_mean
              UpperCamelCase       			=		pointer.normalization.running_var

       if isinstance(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		"MobilenetV1/Logits/Conv2d_1c_1x1/"
              UpperCamelCase       			=		model.classifier.weight
              UpperCamelCase       			=		model.classifier.bias

       return tf_to_pt_map



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       try:
              import numpy as np
              import tensorflow as tf
       except ImportError:
              logger.error(
                  "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
                  "https://www.tensorflow.org/install/ for installation instructions."					)
              raise

       # Load weights from TF model
       UpperCamelCase       			=		tf.train.list_variables(UpperCAmelCase_					)
       UpperCamelCase       			=		{}
       for name, shape in init_vars:
              logger.info(F"Loading TF weight {name} with shape {shape}"					)
              UpperCamelCase       			=		tf.train.load_variable(UpperCAmelCase_		,   UpperCAmelCase_					)
              UpperCamelCase       			=		array

       # Build TF to PyTorch weights loading map
       UpperCamelCase       			=		_build_tf_to_pytorch_map(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       for name, pointer in tf_to_pt_map.items():
              logger.info(F"Importing {name}"					)
              if name not in tf_weights:
                     logger.info(F"{name} not in tf pre-trained weights, skipping"					)
                     continue

              UpperCamelCase       			=		tf_weights[name]

              if "depthwise_weights" in name:
                     logger.info("Transposing depthwise"					)
                     UpperCamelCase       			=		np.transpose(UpperCAmelCase_		,   (2, 3, 0, 1)					)
              elif "weights" in name:
                     logger.info("Transposing"					)
                     if len(pointer.shape					) == 2:  # copying into linear layer
                            UpperCamelCase       			=		array.squeeze().transpose()
                     else:
                            UpperCamelCase       			=		np.transpose(UpperCAmelCase_		,   (3, 2, 0, 1)					)

              if pointer.shape != array.shape:
                     raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"					)

              logger.info(F"Initialize PyTorch weight {name} {array.shape}"					)
              UpperCamelCase       			=		torch.from_numpy(UpperCAmelCase_					)

              tf_weights.pop(UpperCAmelCase_		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/RMSProp"		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/RMSProp_1"		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/ExponentialMovingAverage"		,   UpperCAmelCase_					)

       logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()					)}"					)
       return model



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				torch.Tensor:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		features.shape[-2:]
       UpperCamelCase			,							UpperCamelCase       			=		conv_layer.stride
       UpperCamelCase			,							UpperCamelCase       			=		conv_layer.kernel_size

       if in_height % stride_height == 0:
              UpperCamelCase       			=		max(kernel_height - stride_height		,   0					)
       else:
              UpperCamelCase       			=		max(kernel_height - (in_height % stride_height)		,   0					)

       if in_width % stride_width == 0:
              UpperCamelCase       			=		max(kernel_width - stride_width		,   0					)
       else:
              UpperCamelCase       			=		max(kernel_width - (in_width % stride_width)		,   0					)

       UpperCamelCase       			=		pad_along_width // 2
       UpperCamelCase       			=		pad_along_width - pad_left
       UpperCamelCase       			=		pad_along_height // 2
       UpperCamelCase       			=		pad_along_height - pad_top

       UpperCamelCase       			=		(pad_left, pad_right, pad_top, pad_bottom)
       return nn.functional.pad(UpperCAmelCase_		,   UpperCAmelCase_		,   "constant"		,   0.0					)



class 	__a	(   nn.Module						):






       def __init__(	self			:				List[Any]							,   UpperCAmelCase_			:				MobileNetVaConfig							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[int] = 1							,   UpperCAmelCase_			:				Optional[int] = 1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[bool] = True							,   UpperCAmelCase_			:				Optional[bool or str] = True							,   )->	None:







              """simple docstring"""
              super().__init__()
              UpperCamelCase       			=		config

              if in_channels % groups != 0:
                     raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups."      )
              if out_channels % groups != 0:
                     raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups."      )

              UpperCamelCase       			=		0 if config.tf_padding else int((kernel_size - 1) / 2      )

              UpperCamelCase       			=		nn.Convad(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   groups=UpperCAmelCase_							,   bias=UpperCAmelCase_							,   padding_mode="zeros"							,   )

              if use_normalization:
                     UpperCamelCase       			=		nn.BatchNormad(
                         num_features=UpperCAmelCase_							,   eps=config.layer_norm_eps							,   momentum=0.9997							,   affine=UpperCAmelCase_							,   track_running_stats=UpperCAmelCase_							,   )
              else:
                     UpperCamelCase       			=		None

              if use_activation:
                     if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                            UpperCamelCase       			=		ACTaFN[use_activation]
                     elif isinstance(config.hidden_act							,   UpperCAmelCase_      ):
                            UpperCamelCase       			=		ACTaFN[config.hidden_act]
                     else:
                            UpperCamelCase       			=		config.hidden_act
              else:
                     UpperCamelCase       			=		None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				torch.Tensor      )->	torch.Tensor:







              """simple docstring"""
              if self.config.tf_padding:
                     UpperCamelCase       			=		apply_tf_padding(UpperCAmelCase_							,   self.convolution      )
              UpperCamelCase       			=		self.convolution(UpperCAmelCase_      )
              if self.normalization is not None:
                     UpperCamelCase       			=		self.normalization(UpperCAmelCase_      )
              if self.activation is not None:
                     UpperCamelCase       			=		self.activation(UpperCAmelCase_      )
              return features





class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      MobileNetVaConfig
       UpperCamelCase_		:       Dict				=      load_tf_weights_in_mobilenet_va
       UpperCamelCase_		:       List[str]				=      '''mobilenet_v1'''
       UpperCamelCase_		:       Optional[int]				=      '''pixel_values'''
       UpperCamelCase_		:       List[Any]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Union[nn.Linear, nn.Convad]      )->	None:







              """simple docstring"""
              if isinstance(UpperCAmelCase_							,   (nn.Linear, nn.Convad)      ):
                     module.weight.data.normal_(mean=0.0							,   std=self.config.initializer_range      )
                     if module.bias is not None:
                            module.bias.data.zero_()
              elif isinstance(UpperCAmelCase_							,   nn.BatchNormad      ):
                     module.bias.data.zero_()
                     module.weight.data.fill_(1.0      )





SCREAMING_SNAKE_CASE			  =  R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SCREAMING_SNAKE_CASE			  =  R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''     ,  _lowerCAmelCase     ,  )
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				MobileNetVaConfig							,   UpperCAmelCase_			:				bool = True      )->	Optional[int]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )
              UpperCamelCase       			=		config

              UpperCamelCase       			=		32
              UpperCamelCase       			=		max(int(depth * config.depth_multiplier      )							,   config.min_depth      )

              UpperCamelCase       			=		MobileNetVaConvLayer(
                  UpperCAmelCase_							,   in_channels=config.num_channels							,   out_channels=UpperCAmelCase_							,   kernel_size=3							,   stride=2							,   )

              UpperCamelCase       			=		[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

              UpperCamelCase       			=		nn.ModuleList()
              for i in range(13      ):
                     UpperCamelCase       			=		out_channels

                     if strides[i] == 2 or i == 0:
                            depth *= 2
                            UpperCamelCase       			=		max(int(depth * config.depth_multiplier      )							,   config.min_depth      )

                     self.layer.append(
                         MobileNetVaConvLayer(
                             UpperCAmelCase_							,   in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=3							,   stride=strides[i]							,   groups=UpperCAmelCase_							,   )      )

                     self.layer.append(
                         MobileNetVaConvLayer(
                             UpperCAmelCase_							,   in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=1							,   )      )

              UpperCamelCase       			=		nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

              # Initialize weights and apply final processing
              self.post_init()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Dict      )->	List[str]:







              """simple docstring"""
              raise NotImplementedError






       @add_start_docstrings_to_model_forward(UpperCAmelCase_      )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC							,   output_type=UpperCAmelCase_							,   config_class=_CONFIG_FOR_DOC							,   modality="vision"							,   expected_output=_EXPECTED_OUTPUT_SHAPE							,   )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   )->	Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:







              """simple docstring"""
              UpperCamelCase       			=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase       			=		return_dict if return_dict is not None else self.config.use_return_dict

              if pixel_values is None:
                     raise ValueError("You have to specify pixel_values"      )

              UpperCamelCase       			=		self.conv_stem(UpperCAmelCase_      )

              UpperCamelCase       			=		() if output_hidden_states else None

              for i, layer_module in enumerate(self.layer      ):
                     UpperCamelCase       			=		layer_module(UpperCAmelCase_      )

                     if output_hidden_states:
                            UpperCamelCase       			=		all_hidden_states + (hidden_states,)

              UpperCamelCase       			=		hidden_states

              if self.pooler is not None:
                     UpperCamelCase       			=		torch.flatten(self.pooler(UpperCAmelCase_      )							,   start_dim=1      )
              else:
                     UpperCamelCase       			=		None

              if not return_dict:
                     return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None      )

              return BaseModelOutputWithPoolingAndNoAttention(
                  last_hidden_state=UpperCAmelCase_							,   pooler_output=UpperCAmelCase_							,   hidden_states=UpperCAmelCase_							,   )





@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''     ,  _lowerCAmelCase     ,  )
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				MobileNetVaConfig      )->	None:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )

              UpperCamelCase       			=		config.num_labels
              UpperCamelCase       			=		MobileNetVaModel(UpperCAmelCase_      )

              UpperCamelCase       			=		self.mobilenet_va.layer[-1].convolution.out_channels

              # Classifier head
              UpperCamelCase       			=		nn.Dropout(config.classifier_dropout_prob							,   inplace=UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Linear(UpperCAmelCase_							,   config.num_labels      ) if config.num_labels > 0 else nn.Identity()

              # Initialize weights and apply final processing
              self.post_init()






       @add_start_docstrings_to_model_forward(UpperCAmelCase_      )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT							,   output_type=UpperCAmelCase_							,   config_class=_CONFIG_FOR_DOC							,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,   )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   )->	Union[tuple, ImageClassifierOutputWithNoAttention]:







              """simple docstring"""
              UpperCamelCase       			=		return_dict if return_dict is not None else self.config.use_return_dict

              UpperCamelCase       			=		self.mobilenet_va(UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_							,   return_dict=UpperCAmelCase_      )

              UpperCamelCase       			=		outputs.pooler_output if return_dict else outputs[1]

              UpperCamelCase       			=		self.classifier(self.dropout(UpperCAmelCase_      )      )

              UpperCamelCase       			=		None
              if labels is not None:
                     if self.config.problem_type is None:
                            if self.num_labels == 1:
                                   UpperCamelCase       			=		"regression"
                            elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                                   UpperCamelCase       			=		"single_label_classification"
                            else:
                                   UpperCamelCase       			=		"multi_label_classification"

                     if self.config.problem_type == "regression":
                            UpperCamelCase       			=		MSELoss()
                            if self.num_labels == 1:
                                   UpperCamelCase       			=		loss_fct(logits.squeeze()							,   labels.squeeze()      )
                            else:
                                   UpperCamelCase       			=		loss_fct(UpperCAmelCase_							,   UpperCAmelCase_      )
                     elif self.config.problem_type == "single_label_classification":
                            UpperCamelCase       			=		CrossEntropyLoss()
                            UpperCamelCase       			=		loss_fct(logits.view(-1							,   self.num_labels      )							,   labels.view(-1      )      )
                     elif self.config.problem_type == "multi_label_classification":
                            UpperCamelCase       			=		BCEWithLogitsLoss()
                            UpperCamelCase       			=		loss_fct(UpperCAmelCase_							,   UpperCAmelCase_      )

              if not return_dict:
                     UpperCamelCase       			=		(logits,) + outputs[2:]
                     return ((loss,) + output) if loss is not None else output

              return ImageClassifierOutputWithNoAttention(
                  loss=UpperCAmelCase_							,   logits=UpperCAmelCase_							,   hidden_states=outputs.hidden_states							,   )





